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--- |
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license: apache-2.0 |
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base_model: ayertey01/wav2vec2-large-xlsr-53-AsanteTwi-06second |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice_13_0 |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-xlsr-53-AsanteTwi-07 |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: common_voice_13_0 |
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type: common_voice_13_0 |
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config: tw |
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split: test |
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args: tw |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.4375 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# wav2vec2-large-xlsr-53-AsanteTwi-07 |
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This model is a fine-tuned version of [ayertey01/wav2vec2-large-xlsr-53-AsanteTwi-06second](https://huggingface.co/ayertey01/wav2vec2-large-xlsr-53-AsanteTwi-06second) on the common_voice_13_0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8573 |
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- Wer: 0.4375 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 200 |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.0656 | 16.67 | 100 | 0.6553 | 0.5 | |
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| 0.0354 | 33.33 | 200 | 0.7952 | 0.5 | |
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| 0.0329 | 50.0 | 300 | 0.6705 | 0.4375 | |
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| 0.0261 | 66.67 | 400 | 0.7253 | 0.4375 | |
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| 0.0214 | 83.33 | 500 | 0.8056 | 0.4375 | |
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| 0.0236 | 100.0 | 600 | 0.8573 | 0.4375 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.0 |
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- Tokenizers 0.13.3 |
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